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\section{Conclusion}\label{sec:Conclusion}
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%In this work, we present a general framework for the real-time personalized keyword search on the social network data by leveraging the unique characteristics of the SNPs. We first propose a generic ranking function that consists of the 3 most important dimensions (time,social,textual) to cater user preferences. Then, a fast updating 3D cube index is designed to support powerful pruning on these 3 dimensions simultaneously. On top of the 3D index, we devise a efficient CubeTA algorithm to retrieve the top-k results by the generic ranking function. Several optimizations are further proposed to solve the social distance query. Lastly, the hierarchical partition is deployed on the 3D index to enhance the pruning on the social dimension. To conclude, we provide a generic scheme for the real-time personalized keyword search that can be a good complement to the existing SNPs' search engine.
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%For future work, we aim to provide a distributed version of the framework to handle the extreme large volume of the social network data. In addition, we plan to incorporate heterogenous data, i.e. spatial and media data, into the our search framework for further extension.

In this work, we presented a general framework to support real time personalized keyword search on social networks by leveraging the unique characteristics of the SNPs.
We first proposed a general ranking function that consists of the three most important dimensions (time,social,textual) to cater to various user preferences.
Then, an update-efficient 3D cube index is designed, upon which we devised an efficient Threshold Algorithm called CubeTA. We further proposed several pruning methods in social distance query computation. Extensive experiments on real world social network data have verified the efficiency and scalability of our framework. In future, we plan to further generalize our framework to cater to more types of data like spatial and multimedia data. 